AI’s impact on regulation, operational resilience and customer experience

The session was moderated by Finextra’s Gary Wright and featured speakers Jonathan Ede, director of data technology at CACI; Aman Luther, AI lead at AFME; and Stathis Onasoglou, CFA – EMEA FSI principal at Google Cloud.

Ede started out by summarising the current biggest challenge when it comes to AI adoption. “The best use and adoption of AI is to ensure that it permeates throughout the entire organisation. The real value from AI is to lift it from where we currently see it – in these point-based solutions and siloed implementations of AI – and spread across the entire organisation. Right now we’re significantly limited by integrations. And so AI systems are constrained because they are deployed in single applications, single systems. To really get good value, these AI systems need to be joined together. That also means we need to have better integration of data, aner better availability of data.”

Luther expanded that banks historically have issues with any kind of technology changes considering the amount of technical debt and legacy infrastructure in financial services, and AI is no different.

He continued to explain that, at AFME, they are however seeing a cultural shift. “A lot of banks are very aware of that risk, and they’ve started to hire the right skill set and change their model slightly. For example, we work very much on the risk and governance side, helping banks assess any new proposal and determining whether they should go ahead with it or not. And just that process itself, and banks are coming to us and saying: ‘Hey guys, can you help us with this? How can we look at this?’ Because AI is different to their normal tech appraisal process, we need a whole new process for it.”

Ede continued that AI is different from other technical updates, and goes beyond just looking at ROI. “What we’re seeing here is that the strategy around AI needs to be elevated above the CTO, above the CIO. It sits very much with the CEO because this is not about technology anymore. It’s not about data, not about insight. It’s about, fundamentally, how organisations act, how they how they go about their business. And I think ultimately, it’s really quite difficult for anybody to really grasp.”

Onasoglou expanded on Ede’s commentary. “The term ROI was mentioned, and return on investment is not always financial. So usually some kind of value can be articulated, either top-line revenues or cost cutting, cost mitigation, or even with more abstract terms, with innovation, for example, customer experience. But ultimately, all these things might lead to financial benefits.”

The conversation then turned toward governance and regulation. Luther started by saying that, while people might argue against it, regulation does slow down innovation. He cited the UK, US and EU as examples of regions going about AI in different ways. Frameworks like the EU AI Act will put additional burdens on organisations, especially smaller companies. And while it’s necessary to draw boundaries, the question becomes a societal one: Where do we draw the line?

Ede concurred: “Does regulation ever promote innovation? I’m not necessarily sure it does. I think the regulation that has been proposed so far is pragmatic, and I don’t think it stifles innovation. AI is so fast moving, we need to have guardrails, but those regulations also need to be fast moving. I think you do find that regulations are holding back the growth of AI technologies within the UK. We need to look at that, because we aren’t going to see those same constraints in other nations, such as China. And so we need to make sure where those regulations are adapting to ensure they aren’t stifling future innovation.”

Onasoglou then referred to research Google recently conducted among 340 senior decision makers across retail and commercial banking. When talking about AI blockers and impediments, they found that the “top two factors were lack of clean, analysis-ready data – organisations are data rich but insights poor – and a lack of regulatory insights. Not necessarily restrictive regulation, but a lack of clarity in the landscape.”

Lastly, the panel turned towards balancing AI’s impact on the customer with an organisation’s operational resilience. Onasoglou explained that as a ‘recovering consultant’ he likes to look at things in an action priority matrix, and that he found that operational efficiency and customer experience are not competing priorities.

Luther explained that, coming from the wholesale banking side, they are seeing fewer customer facing use cases. When it comes to operational efficiency, “we’ve seen a huge push and a huge gain. Some of our members are using AI for things like detecting and predicting failed transactions, for example. We’ve seen firms deploying AI there to predict which transaction is likely to fail and which one isn’t, in order to deal with it before fails in the first place. And that creates a huge bottom line benefit, both for their firm as well as firms down the road.”

Ede explained that, working almost exclusively with retail banks, he’s seen lots of gains in the customer experience area. “Always start with the customer experience that leads all the way to personalising experiences. And what we find, from the front-end systems, is that the customer experience is being dramatically changed and improved with the use of AI. But what we’re not seeing just yet is that going one step back again from that. So taking those improvements to customer experience, and making sure that goes all the way back into how the organisation speaks itself internally, and how to organise themselves to ensure they are more joined up. Organisations are not yet treating themselves how they treat their customers.”

Wright concluded the session: “There’s a lot of strategies that need to change – modernisation strategies or transformation strategies that respond to regulation, to competition, and to customer needs – and AI is a considerable part of that.”